A Stacked Ensemble Multi-Label Model for Predicting Co-Occurring Microvascular and Macrovascular Complications in Type 2 Diabetes.

Background: Type 2 diabetes mellitus (T2DM) leads to severe microvascular and macrovascular complications. Traditional single-label prediction models fail to capture their co-occurring nature. This study develops a stacked ensemble multi-label framework that integrates established machine learning techniques into a unified and clinically interpretable approach for the joint prediction of diabetes complications. Methods: In a retrospective study of 965 T2DM patients, complications were aggregated into microvascular (retinopathy, nephropathy, and neuropathy) and macrovascular (cardiovascular, cerebrovascular) categories. A class-weighted stacking ensemble integrated three base models-Random Forest, Light GBM, and Cat Boost-within Binary Relevance (BR) and Classifier Chain (CC) frameworks, with a multi-output logistic regression meta-learner. Performance was evaluated via 5-fold cross-validation (Hamming Loss, F1-score, area under the curve [AUC]). SHapley Additive exPlanations (SHAP) analysis elucidated risk factors. Results: The Stacking-CC model achieved superior performance, with an overall F1-score of 0.752 ± 0.049 and AUC of 0.857 ± 0.032. A moderate correlation ( Conclusion: The Stacking-CC framework effectively models co-occurring diabetic complications with high accuracy and interpretability. By delineating distinct risk hierarchies, it enables the development of targeted, complication-specific management strategies.